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(from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n", "Installing collected packages: tokenizers, sentencepiece, xxhash, urllib3, multiprocess, responses, huggingface-hub, transformers, datasets\n", " Attempting uninstall: urllib3\n", " Found existing installation: urllib3 1.24.3\n", " Uninstalling urllib3-1.24.3:\n", " Successfully uninstalled urllib3-1.24.3\n", "Successfully installed datasets-2.9.0 huggingface-hub-0.12.0 multiprocess-0.70.14 responses-0.18.0 sentencepiece-0.1.97 tokenizers-0.13.2 transformers-4.26.1 urllib3-1.26.14 xxhash-3.2.0\n" ] } ], "source": [ "!pip install transformers datasets torch sentencepiece" ] }, { "cell_type": "markdown", "source": [ "# Załadowanie datasetu" ], "metadata": { "id": "dhN0rmb5Oi3d" } }, { "cell_type": "code", "source": [ "from datasets import load_dataset" ], "metadata": { "id": "tnaDkwZ2Pbnn" }, "execution_count": 2, "outputs": [] }, { "cell_type": "code", "source": [ "dataset = load_dataset(\"sms_spam\")" ], "metadata": { 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"b18c24667e4348389bd0abfbbb84747b", "3e95b7edd1704ed1b28245c560035492", "ebfffc894e774299b9870c67df0dca0f", "dcd34a760b324e6a94e219a9e10e557b", "b20d9f84deb5440698b832c8af79d148", "2a561a1680364ab9b7bd4221ff30f98f", "e2ba3f5319a14d65b28d2a401610eee2", "eb1ebdb33f8748b797f45dc6c839ad44", "481f3e3471a04dc692116ff3eac472b9", "1d3326424eda448a969a913526ff138b", "f71bbb5a6ec94d05a55bca2c8d9609e6", "d5901c24329f41f99a7b6ac4901a08c5", "667aeb90d1be422f991d1a689be3d69e", "6f721a22c07342bc9e9ba850d3bfa261" ] }, "id": "cCiAuRqrOkvV", "outputId": "87f24c1e-cb25-4b5a-b786-6f3bcbc0b96f" }, "execution_count": 3, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading builder script: 0%| | 0.00/3.21k [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "99a8b62c0eb9498a801d33b890ff7bed" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Downloading metadata: 0%| | 0.00/1.69k [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "0b9435ced6e64d5b9a8817c10800df73" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Downloading readme: 0%| | 0.00/4.87k [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "748c273b887042158812dc3ac1491537" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Downloading and preparing dataset sms_spam/plain_text to /root/.cache/huggingface/datasets/sms_spam/plain_text/1.0.0/53f051d3b5f62d99d61792c91acefe4f1577ad3e4c216fb0ad39e30b9f20019c...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Downloading data: 0%| | 0.00/203k [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "c3f39c4d26334407bf94756b5111bafa" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Generating train split: 0%| | 0/5574 [00:00, ? examples/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "52eb6c7623a34694a19e12a88cff244e" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Dataset sms_spam downloaded and prepared to /root/.cache/huggingface/datasets/sms_spam/plain_text/1.0.0/53f051d3b5f62d99d61792c91acefe4f1577ad3e4c216fb0ad39e30b9f20019c. Subsequent calls will reuse this data.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ " 0%| | 0/1 [00:00, ?it/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "dcd34a760b324e6a94e219a9e10e557b" } }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "dataset['train'][0]" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "JKFHPko3OnAV", "outputId": "a94fa7c1-ab93-4473-d06a-61a8c50d8783" }, "execution_count": 4, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'sms': 'Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\\n',\n", " 'label': 0}" ] }, "metadata": {}, "execution_count": 4 } ] }, { "cell_type": "markdown", "source": [ "# Modyfikacja datasetu - klasyfikacja" ], "metadata": { "id": "l140vJrgYxPr" } }, { "cell_type": "code", "source": [ "parsed_dataset = []\n", "\n", "for row in dataset['train']:\n", " text = row['sms']\n", " new_row = {}\n", " new_row['sms'] = text\n", " if row['label'] == 0:\n", " new_row['label'] = \"conversation\"\n", " else:\n", " new_row['label'] = \"advertising\"\n", " parsed_dataset.append(new_row)\n", "\n", "parsed_dataset[0]" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1boUF-YiY3_y", "outputId": "15412aef-de85-43ce-ad3b-f88283a242a0" }, "execution_count": 5, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'sms': 'Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\\n',\n", " 'label': 'conversation'}" ] }, "metadata": {}, "execution_count": 5 } ] }, { "cell_type": "markdown", "source": [ "# Tokenizer T5" ], "metadata": { "id": "O-J-jBDxPJcn" } }, { "cell_type": "code", "source": [ "from transformers import T5Tokenizer" ], "metadata": { "id": "P23AYPX1PZ6g" }, "execution_count": 6, "outputs": [] }, { "cell_type": "code", "source": [ "tokenizer = T5Tokenizer.from_pretrained('t5-base')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 185, "referenced_widgets": [ "54ce84849a974330a66e1100086a8fed", "4efdba3661464381a034ba90eed898dc", "d6e70f45ef8f426e8a9f1646e4bfdb2f", "a6b434b5186341bbb2e3a6b5772f307f", "eb2079320deb4ef5aa5624e164689b4e", "88f741767e6845dda901e2f17fd978e1", "d8bd3441499c405cbea7b6b61f7636ad", "af6d44105eb8435cbc34fa902e4303b9", "e5dc73086a7d48a5a75274ecf7e1e83e", "7bad80b954c74f8995f9c572a2831b6f", "6358c42c34a74dfc8df0101076eb2274", "3806842991b04a19a315223c4f0d05b0", "0f9e34991e634982a37ed4474939a614", "6fd3d1538419439db78ae1940e6ecd95", "968e624fbe4b49d8b22ba35237f29f04", "490d54e698024024a0376e0c5aa57afa", "9c921a01f9f44ef6b6b0867fbce29d4e", "d932c14e5642431da3490fda711b855a", "e66eea96fb8d467085ff05b14cef41f5", "8ff90b6f1b204ceaa41c09c48bdb4a95", "1c3c1fd4ebbf4363aa9c7b7df4fb96a7", "7febee22767b48ed8ffb31b2e86e2bde" ] }, "id": "q5Jz0E_oPMBr", "outputId": "57650e4c-558f-46aa-e08f-0362ab53e2e8" }, "execution_count": 7, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading (…)ve/main/spiece.model: 0%| | 0.00/792k [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "54ce84849a974330a66e1100086a8fed" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Downloading (…)lve/main/config.json: 0%| | 0.00/1.21k [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "3806842991b04a19a315223c4f0d05b0" } }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.8/dist-packages/transformers/models/t5/tokenization_t5.py:163: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n", "For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n", "- Be aware that you SHOULD NOT rely on t5-base automatically truncating your input to 512 when padding/encoding.\n", "- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n", "- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n", " warnings.warn(\n" ] } ] }, { "cell_type": "code", "source": [ "sms = parsed_dataset[0]['sms']\n", "print('Original: ', sms)\n", "print('Tokenized: ', tokenizer.tokenize(sms))\n", "print('Token IDs: ', tokenizer.convert_tokens_to_ids(tokenizer.tokenize(sms)))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dfxJQpoePsvI", "outputId": "713d0aed-1350-44f9-e59d-e4a2f8b7a0b3" }, "execution_count": 8, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Original: Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\n", "\n", "Tokenized: ['▁Go', '▁until', '▁jur', 'ong', '▁point', ',', '▁crazy', '.', '.', '▁Available', '▁only', '▁in', '▁bug', 'is', '▁', 'n', '▁great', '▁world', '▁la', '▁', 'e', '▁buffet', '...', '▁Cine', '▁there', '▁got', '▁', 'a', 'more', '▁wa', 't', '...']\n", "Token IDs: [1263, 552, 10081, 2444, 500, 6, 6139, 5, 5, 8144, 163, 16, 8143, 159, 3, 29, 248, 296, 50, 3, 15, 15385, 233, 17270, 132, 530, 3, 9, 3706, 8036, 17, 233]\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Check maximum lenght of a sentence" ], "metadata": { "id": "UpluhM8cU5Ir" } }, { "cell_type": "code", "source": [ "max_len = 0\n", "\n", "for sentence in parsed_dataset:\n", " input_ids = tokenizer.encode(sentence['sms'], add_special_tokens=True)\n", " max_len = max(max_len, len(input_ids))\n", "\n", "print('Max sentence length: ', max_len)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7uNUkixPU85O", "outputId": "6a83d1cb-629d-4725-e3a2-5c60f9962108" }, "execution_count": 9, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Max sentence length: 338\n" ] } ] }, { "cell_type": "code", "source": [ "max_label_len = 0\n", "\n", "for sentence in parsed_dataset:\n", " input_ids = tokenizer.encode(sentence['label'], add_special_tokens=True)\n", " max_label_len = max(max_label_len, len(input_ids))\n", "\n", "print('Max sentence length: ', max_label_len)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "lj0issBznZfK", "outputId": "38498917-ba97-472f-9012-5da83babab62" }, "execution_count": 10, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Max sentence length: 2\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Pre train tokenization" ], "metadata": { "id": "nfw62HdgSERb" } }, { "cell_type": "code", "source": [ "import torch" ], "metadata": { "id": "KTXYalS1VLqH" }, "execution_count": 11, "outputs": [] }, { "cell_type": "code", "source": [ "input_ids = []\n", "target_ids = []\n", "attention_masks = []\n", "\n", "for sentence in parsed_dataset:\n", " encoded_dict = tokenizer.encode_plus(\n", " sentence['sms'],\n", " add_special_tokens = True,\n", " max_length = 340,\n", " padding = 'max_length',\n", " truncation=True,\n", " return_attention_mask = True,\n", " return_tensors = 'pt',\n", " )\n", " \n", " encoded_target_dict = tokenizer.encode_plus(\n", " sentence['label'],\n", " add_special_tokens = True,\n", " max_length = 2,\n", " padding = 'max_length',\n", " truncation=True,\n", " return_attention_mask = True,\n", " return_tensors = 'pt',\n", " )\n", " \n", " input_ids.append(encoded_dict['input_ids'])\n", " target_ids.append(encoded_target_dict['input_ids'])\n", " attention_masks.append(encoded_dict['attention_mask'])\n", "\n", "input_ids = torch.cat(input_ids, dim=0)\n", "target_ids = torch.cat(target_ids, dim=0)\n", "attention_masks = torch.cat(attention_masks, dim=0)\n", "\n", "print('Original: ', parsed_dataset[0])\n", "print('Token IDs:', input_ids[0])\n", "print('Label token IDs:', target_ids[0])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Z28QYfLnSGxR", "outputId": "9b98f987-7176-48cc-d298-f09f69c6eab7" }, "execution_count": 12, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Original: {'sms': 'Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\\n', 'label': 'conversation'}\n", "Token IDs: tensor([ 1263, 552, 10081, 2444, 500, 6, 6139, 5, 5, 8144,\n", " 163, 16, 8143, 159, 3, 29, 248, 296, 50, 3,\n", " 15, 15385, 233, 17270, 132, 530, 3, 9, 3706, 8036,\n", " 17, 233, 1, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", "Label token IDs: tensor([3634, 1])\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Split dataset" ], "metadata": { "id": "qD_t0y0KVVSy" } }, { "cell_type": "code", "source": [ "from torch.utils.data import TensorDataset, random_split" ], "metadata": { "id": "vN_SatRIVa4c" }, "execution_count": 13, "outputs": [] }, { "cell_type": "code", "source": [ "dataset = TensorDataset(input_ids, attention_masks, target_ids)\n", "\n", "test_size = 1000\n", "dataset_len = len(dataset)\n", "train_size = int(0.9 * (dataset_len-test_size))\n", "val_size = (dataset_len-test_size) - train_size\n", "\n", "test_dataset, train_dataset, val_dataset = random_split(dataset, [test_size, train_size, val_size])\n", "\n", "print('{:>5,} test samples'.format(test_size))\n", "print('{:>5,} training samples'.format(train_size))\n", "print('{:>5,} validation samples'.format(val_size))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Mm6vc6lLVW3l", "outputId": "cfb15fb6-1daa-4b3c-df1b-5d0c862e8821" }, "execution_count": 14, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "1,000 test samples\n", "4,116 training samples\n", " 458 validation samples\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Create train and validation loaders" ], "metadata": { "id": "bmgQOP4EVfA1" } }, { "cell_type": "code", "source": [ "from torch.utils.data import DataLoader, RandomSampler, SequentialSampler" ], "metadata": { "id": "CxnQ3cmIVlNh" }, "execution_count": 15, "outputs": [] }, { "cell_type": "code", "source": [ "batch_size = 16\n", "\n", "train_dataloader = DataLoader(\n", " train_dataset,\n", " sampler = RandomSampler(train_dataset),\n", " batch_size = batch_size\n", " )\n", "\n", "validation_dataloader = DataLoader(\n", " val_dataset,\n", " sampler = SequentialSampler(val_dataset),\n", " batch_size = batch_size\n", " )" ], "metadata": { "id": "0hcpO_onVjEC" }, "execution_count": 16, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Device check" ], "metadata": { "id": "efwhqLyyVu9z" } }, { "cell_type": "code", "source": [ "if torch.cuda.is_available(): \n", " device = torch.device(\"cuda\")\n", "\n", " print('There are %d GPU(s) available.' % torch.cuda.device_count())\n", " print('We will use the GPU:', torch.cuda.get_device_name(0))\n", "\n", "else:\n", " print('No GPU available, using the CPU instead.')\n", " device = torch.device(\"cpu\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ANBCfNGnVwVk", "outputId": "8db82471-22b2-450d-cb9d-ba86ce765fa2" }, "execution_count": 17, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "There are 1 GPU(s) available.\n", "We will use the GPU: Tesla T4\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Load T5 model" ], "metadata": { "id": "okTx_ynMV0rH" } }, { "cell_type": "code", "source": [ "from transformers import T5ForConditionalGeneration" ], "metadata": { "id": "Eu-7Eed8WgN0" }, "execution_count": 18, "outputs": [] }, { "cell_type": "code", "source": [ "model = T5ForConditionalGeneration.from_pretrained('t5-base')\n", "\n", "model.cuda()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "c4238a30a23f4a4995a64596a076f639", "7008395271204caba9d2a70e886f3fb7", "91fe75c3703e49e2b9200eb32465979a", "605fe093ee154e1eafda5f92c07712ef", "63343a6f0a2a4f66b878afc18d408c1c", "fc24763fbd70457fbb2a67883ec38a67", "7ba8e63a4e1645ebad9829af1706fb1d", "1aeec94f6aed4d6fa9ef94b5e8011f95", "7ab80f2dc4bd4bc4b4d28bff719068e2", "ab5aa9e15b894bf6aec2ed8d7949d4fb", "5aff4f72159e4187adf00959d8863017", "52e83e2e748f4c79b789d0354f4e941b", "ef0f5971cb444d8f8c5c1ace4d8ebe81", "64885f8157264a1297bab372bf8a7fb5", "6b3444ddd6d24f448631e3e86d992241", "7e0aaf3e4b5b4bbc9c8120d9e5c00d8d", "51c2d890c6874141a0d5ecc0eb89a282", "1ec01f40f98f47e3aae79a9f871d9df1", "f208555568824dd9a800555cc17182be", "5fd3c6d56ef644ebaff4da4bffa470a5", "5167e08d346f47468249d2f40b262792", "904d0e8e3a2b443f9e90652d78ecff95" ] }, "id": "JKv9O8kfV2zZ", "outputId": "0d41faa2-6857-4a67-d581-41383ffc0378" }, "execution_count": 19, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading (…)\"pytorch_model.bin\";: 0%| | 0.00/892M [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "c4238a30a23f4a4995a64596a076f639" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Downloading (…)neration_config.json: 0%| | 0.00/147 [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "52e83e2e748f4c79b789d0354f4e941b" } }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "T5ForConditionalGeneration(\n", " (shared): Embedding(32128, 768)\n", " (encoder): T5Stack(\n", " (embed_tokens): Embedding(32128, 768)\n", " (block): ModuleList(\n", " (0): T5Block(\n", " (layer): ModuleList(\n", " (0): T5LayerSelfAttention(\n", " (SelfAttention): T5Attention(\n", " (q): Linear(in_features=768, out_features=768, bias=False)\n", " (k): Linear(in_features=768, out_features=768, bias=False)\n", " (v): Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " (relative_attention_bias): Embedding(32, 12)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (1): T5LayerFF(\n", " (DenseReluDense): T5DenseActDense(\n", " (wi): Linear(in_features=768, out_features=3072, bias=False)\n", " (wo): Linear(in_features=3072, out_features=768, bias=False)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " (act): ReLU()\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " )\n", " (1): T5Block(\n", " (layer): ModuleList(\n", " (0): T5LayerSelfAttention(\n", " (SelfAttention): T5Attention(\n", " (q): Linear(in_features=768, out_features=768, bias=False)\n", " (k): Linear(in_features=768, out_features=768, bias=False)\n", " (v): Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (1): T5LayerFF(\n", " (DenseReluDense): T5DenseActDense(\n", " (wi): Linear(in_features=768, out_features=3072, bias=False)\n", " (wo): Linear(in_features=3072, out_features=768, bias=False)\n", " (dropout): 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Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (1): T5LayerCrossAttention(\n", " (EncDecAttention): T5Attention(\n", " (q): Linear(in_features=768, out_features=768, bias=False)\n", " (k): Linear(in_features=768, out_features=768, bias=False)\n", " (v): Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (2): T5LayerFF(\n", " (DenseReluDense): T5DenseActDense(\n", " (wi): Linear(in_features=768, out_features=3072, bias=False)\n", " (wo): Linear(in_features=3072, out_features=768, bias=False)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " (act): ReLU()\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " )\n", " (7): T5Block(\n", " (layer): ModuleList(\n", " (0): T5LayerSelfAttention(\n", " (SelfAttention): T5Attention(\n", " (q): Linear(in_features=768, out_features=768, bias=False)\n", " (k): Linear(in_features=768, out_features=768, bias=False)\n", " (v): Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (1): T5LayerCrossAttention(\n", " (EncDecAttention): T5Attention(\n", " (q): Linear(in_features=768, out_features=768, bias=False)\n", " (k): Linear(in_features=768, out_features=768, bias=False)\n", " (v): Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (2): T5LayerFF(\n", " (DenseReluDense): T5DenseActDense(\n", " (wi): Linear(in_features=768, out_features=3072, bias=False)\n", " (wo): Linear(in_features=3072, out_features=768, bias=False)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " (act): ReLU()\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " )\n", " (8): T5Block(\n", " (layer): ModuleList(\n", " (0): T5LayerSelfAttention(\n", " (SelfAttention): T5Attention(\n", " (q): Linear(in_features=768, out_features=768, bias=False)\n", " (k): Linear(in_features=768, out_features=768, bias=False)\n", " (v): Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (1): T5LayerCrossAttention(\n", " (EncDecAttention): T5Attention(\n", " (q): Linear(in_features=768, out_features=768, bias=False)\n", " (k): Linear(in_features=768, out_features=768, bias=False)\n", " (v): Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (2): T5LayerFF(\n", " (DenseReluDense): T5DenseActDense(\n", " (wi): Linear(in_features=768, out_features=3072, bias=False)\n", " (wo): Linear(in_features=3072, out_features=768, bias=False)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " (act): ReLU()\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " )\n", " (9): T5Block(\n", " (layer): ModuleList(\n", " (0): T5LayerSelfAttention(\n", " (SelfAttention): T5Attention(\n", " (q): Linear(in_features=768, out_features=768, bias=False)\n", " (k): Linear(in_features=768, out_features=768, bias=False)\n", " (v): Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (1): T5LayerCrossAttention(\n", " (EncDecAttention): T5Attention(\n", " (q): Linear(in_features=768, out_features=768, bias=False)\n", " (k): Linear(in_features=768, out_features=768, bias=False)\n", " (v): Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (2): T5LayerFF(\n", " (DenseReluDense): T5DenseActDense(\n", " (wi): Linear(in_features=768, out_features=3072, bias=False)\n", " (wo): Linear(in_features=3072, out_features=768, bias=False)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " (act): ReLU()\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " )\n", " (10): T5Block(\n", " (layer): ModuleList(\n", " (0): T5LayerSelfAttention(\n", " (SelfAttention): T5Attention(\n", " (q): Linear(in_features=768, out_features=768, bias=False)\n", " (k): Linear(in_features=768, out_features=768, bias=False)\n", " (v): Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (1): T5LayerCrossAttention(\n", " (EncDecAttention): T5Attention(\n", " (q): Linear(in_features=768, out_features=768, bias=False)\n", " (k): Linear(in_features=768, out_features=768, bias=False)\n", " (v): Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (2): T5LayerFF(\n", " (DenseReluDense): T5DenseActDense(\n", " (wi): Linear(in_features=768, out_features=3072, bias=False)\n", " (wo): Linear(in_features=3072, out_features=768, bias=False)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " (act): ReLU()\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " )\n", " (11): T5Block(\n", " (layer): ModuleList(\n", " (0): T5LayerSelfAttention(\n", " (SelfAttention): T5Attention(\n", " (q): Linear(in_features=768, out_features=768, bias=False)\n", " (k): Linear(in_features=768, out_features=768, bias=False)\n", " (v): Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (1): T5LayerCrossAttention(\n", " (EncDecAttention): T5Attention(\n", " (q): Linear(in_features=768, out_features=768, bias=False)\n", " (k): Linear(in_features=768, out_features=768, bias=False)\n", " (v): Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (2): T5LayerFF(\n", " (DenseReluDense): T5DenseActDense(\n", " (wi): Linear(in_features=768, out_features=3072, bias=False)\n", " (wo): Linear(in_features=3072, out_features=768, bias=False)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " (act): ReLU()\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " )\n", " )\n", " (final_layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (lm_head): Linear(in_features=768, out_features=32128, bias=False)\n", ")" ] }, "metadata": {}, "execution_count": 19 } ] }, { "cell_type": "markdown", "source": [ "# Helper functions" ], "metadata": { "id": "F_SDAwxoawDy" } }, { "cell_type": "code", "source": [ "import datetime\n", "import numpy as np" ], "metadata": { "id": "s-q6_F38bLVA" }, "execution_count": 20, "outputs": [] }, { "cell_type": "code", "source": [ "def calculate_accuracy(preds, target):\n", " results_ok = 0.0\n", " results_false = 0.0\n", "\n", " for idx, pred in enumerate(preds):\n", " if pred == target[idx]:\n", " results_ok += 1.0\n", " else:\n", " results_false += 1.0\n", "\n", " return results_ok / (results_ok + results_false)\n", "\n", "def format_time(elapsed):\n", " '''\n", " Takes a time in seconds and returns a string hh:mm:ss\n", " '''\n", " elapsed_rounded = int(round((elapsed)))\n", " return str(datetime.timedelta(seconds=elapsed_rounded))" ], "metadata": { "id": "FzUi8908ax61" }, "execution_count": 21, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Init training" ], "metadata": { "id": "ucChBa-9bXJy" } }, { "cell_type": "code", "source": [ "from transformers import get_linear_schedule_with_warmup" ], "metadata": { "id": "c9e7rbGwbdEp" }, "execution_count": 22, "outputs": [] }, { "cell_type": "code", "source": [ "optimizer = torch.optim.AdamW(model.parameters(),\n", " lr = 3e-4,\n", " eps = 1e-8\n", " )\n", "\n", "epochs = 4\n", "\n", "total_steps = len(train_dataloader) * epochs\n", "\n", "scheduler = get_linear_schedule_with_warmup(optimizer, \n", " num_warmup_steps = 0,\n", " num_training_steps = total_steps)" ], "metadata": { "id": "A7XUF4PNbYy8" }, "execution_count": 23, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Training" ], "metadata": { "id": "DAzQWODja0A3" } }, { "cell_type": "code", "source": [ "import random\n", "import time" ], "metadata": { "id": "Hoa7NlU0bI7G" }, "execution_count": 24, "outputs": [] }, { "cell_type": "code", "source": [ "# This training code is based on the `run_glue.py` script here:\n", "# https://github.com/huggingface/transformers/blob/5bfcd0485ece086ebcbed2d008813037968a9e58/examples/run_glue.py#L128\n", "\n", "seed_val = 42\n", "\n", "random.seed(seed_val)\n", "np.random.seed(seed_val)\n", "torch.manual_seed(seed_val)\n", "torch.cuda.manual_seed_all(seed_val)\n", "\n", "training_stats = []\n", "total_t0 = time.time()\n", "\n", "for epoch_i in range(0, epochs):\n", " \n", " # ========================================\n", " # Training\n", " # ========================================\n", "\n", " print(\"\")\n", " print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))\n", " print('Training...')\n", "\n", " t0 = time.time()\n", " total_train_loss = 0\n", "\n", " model.train()\n", "\n", " for step, batch in enumerate(train_dataloader):\n", " if step % 40 == 0 and not step == 0:\n", " elapsed = format_time(time.time() - t0)\n", " print(' Batch {:>5,} of {:>5,}. Elapsed: {:}.'.format(step, len(train_dataloader), elapsed))\n", "\n", " b_input_ids = batch[0].to(device)\n", " b_input_mask = batch[1].to(device)\n", "\n", " y = batch[2].to(device)\n", " y_ids = y[:, :-1].contiguous()\n", " lm_labels = y[:, 1:].clone().detach()\n", " lm_labels[y[:, 1:] == tokenizer.pad_token_id] = -100\n", "\n", " model.zero_grad() \n", "\n", " outputs = model(\n", " input_ids=b_input_ids,\n", " attention_mask=b_input_mask,\n", " decoder_input_ids=y_ids,\n", " labels=lm_labels\n", " )\n", "\n", " loss = outputs['loss']\n", " total_train_loss += loss.item()\n", "\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n", "\n", " optimizer.step()\n", " scheduler.step()\n", "\n", " avg_train_loss = total_train_loss / len(train_dataloader) \n", " training_time = format_time(time.time() - t0)\n", "\n", " print(\"\")\n", " print(\" Average training loss: {0:.2f}\".format(avg_train_loss))\n", " print(\" Training epcoh took: {:}\".format(training_time))\n", " \n", " # ========================================\n", " # Validation\n", " # ========================================\n", "\n", " print(\"\")\n", " print(\"Running Validation...\")\n", "\n", " t0 = time.time()\n", " model.eval()\n", "\n", " total_eval_loss = 0\n", " total_eval_accuracy = 0\n", "\n", " for batch in validation_dataloader:\n", " b_input_ids = batch[0].to(device)\n", " b_input_mask = batch[1].to(device)\n", "\n", " y = batch[2].to(device)\n", " y_ids = y[:, :-1].contiguous()\n", " lm_labels = y[:, 1:].clone().detach()\n", " lm_labels[y[:, 1:] == tokenizer.pad_token_id] = -100\n", " \n", " with torch.no_grad(): \n", "\n", " outputs = model(\n", " input_ids=b_input_ids,\n", " attention_mask=b_input_mask,\n", " decoder_input_ids=y_ids,\n", " labels=lm_labels\n", " )\n", "\n", " loss = outputs['loss']\n", " total_eval_loss += loss.item()\n", "\n", " generated_ids = model.generate(\n", " input_ids = b_input_ids,\n", " attention_mask = b_input_mask, \n", " max_length=2, \n", " num_beams=2,\n", " repetition_penalty=2.5, \n", " length_penalty=1.0, \n", " early_stopping=True\n", " )\n", "\n", " preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]\n", " target = [tokenizer.decode(t, skip_special_tokens=True, clean_up_tokenization_spaces=True) for t in y]\n", " total_eval_accuracy += calculate_accuracy(preds, target) \n", "\n", " avg_val_loss = total_eval_loss / len(validation_dataloader)\n", "\n", " avg_val_accuracy = total_eval_accuracy / len(validation_dataloader)\n", " print(\" Accuracy: {0:.2f}\".format(avg_val_accuracy))\n", " \n", " validation_time = format_time(time.time() - t0)\n", " print(\" Validation took: {:}\".format(validation_time))\n", " print(\" Validation Loss: {0:.2f}\".format(avg_val_loss))\n", "\n", " training_stats.append(\n", " {\n", " 'epoch': epoch_i + 1,\n", " 'Training Loss': avg_train_loss,\n", " 'Valid. Loss': avg_val_loss,\n", " 'Valid. Accur.': avg_val_accuracy,\n", " 'Training Time': training_time,\n", " 'Validation Time': validation_time\n", " }\n", " )\n", "\n", "print(\"\")\n", "print(\"Training complete!\")\n", "\n", "print(\"Total training took {:} (h:mm:ss)\".format(format_time(time.time()-total_t0)))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "xsHxfslka1u5", "outputId": "c1d90548-6d70-4172-e0e2-e916eea141a6" }, "execution_count": 25, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "======== Epoch 1 / 4 ========\n", "Training...\n", " Batch 40 of 258. Elapsed: 0:00:46.\n", " Batch 80 of 258. Elapsed: 0:01:32.\n", " Batch 120 of 258. Elapsed: 0:02:17.\n", " Batch 160 of 258. Elapsed: 0:03:02.\n", " Batch 200 of 258. Elapsed: 0:03:47.\n", " Batch 240 of 258. Elapsed: 0:04:33.\n", "\n", " Average training loss: 0.02\n", " Training epcoh took: 0:04:52\n", "\n", "Running Validation...\n", " Accuracy: 0.00\n", " Validation took: 0:00:24\n", " Validation Loss: 0.00\n", "\n", "======== Epoch 2 / 4 ========\n", "Training...\n", " Batch 40 of 258. Elapsed: 0:00:45.\n", " Batch 80 of 258. Elapsed: 0:01:31.\n", " Batch 120 of 258. Elapsed: 0:02:16.\n", " Batch 160 of 258. Elapsed: 0:03:01.\n", " Batch 200 of 258. Elapsed: 0:03:46.\n", " Batch 240 of 258. Elapsed: 0:04:32.\n", "\n", " Average training loss: 0.00\n", " Training epcoh took: 0:04:52\n", "\n", "Running Validation...\n", " Accuracy: 0.00\n", " Validation took: 0:00:24\n", " Validation Loss: 0.00\n", "\n", "======== Epoch 3 / 4 ========\n", "Training...\n", " Batch 40 of 258. Elapsed: 0:00:45.\n", " Batch 80 of 258. Elapsed: 0:01:30.\n", " Batch 120 of 258. Elapsed: 0:02:15.\n", " Batch 160 of 258. Elapsed: 0:03:01.\n", " Batch 200 of 258. Elapsed: 0:03:46.\n", " Batch 240 of 258. Elapsed: 0:04:31.\n", "\n", " Average training loss: 0.00\n", " Training epcoh took: 0:04:51\n", "\n", "Running Validation...\n", " Accuracy: 0.00\n", " Validation took: 0:00:24\n", " Validation Loss: 0.00\n", "\n", "======== Epoch 4 / 4 ========\n", "Training...\n", " Batch 40 of 258. Elapsed: 0:00:45.\n", " Batch 80 of 258. Elapsed: 0:01:30.\n", " Batch 120 of 258. Elapsed: 0:02:16.\n", " Batch 160 of 258. Elapsed: 0:03:01.\n", " Batch 200 of 258. Elapsed: 0:03:46.\n", " Batch 240 of 258. Elapsed: 0:04:31.\n", "\n", " Average training loss: 0.00\n", " Training epcoh took: 0:04:51\n", "\n", "Running Validation...\n", " Accuracy: 0.00\n", " Validation took: 0:00:24\n", " Validation Loss: 0.00\n", "\n", "Training complete!\n", "Total training took 0:21:01 (h:mm:ss)\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Train summary" ], "metadata": { "id": "xIpFPoRb91Or" } }, { "cell_type": "code", "source": [ "import pandas as pd\n", "\n", "pd.set_option('precision', 2)\n", "df_stats = pd.DataFrame(data=training_stats)\n", "\n", "df_stats = df_stats.set_index('epoch')\n", "df_stats" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "GjYqBrrO93Oh", "outputId": "4a9cd46d-4c7c-447e-f98d-21f3cdd66c34" }, "execution_count": 26, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Training Loss Valid. Loss Valid. Accur. Training Time Validation Time\n", "epoch \n", "1 1.84e-02 0.0 0.0 0:04:52 0:00:24\n", "2 1.49e-06 0.0 0.0 0:04:52 0:00:24\n", "3 4.64e-07 0.0 0.0 0:04:51 0:00:24\n", "4 1.43e-07 0.0 0.0 0:04:51 0:00:24" ], "text/html": [ "\n", "
\n", " | Training Loss | \n", "Valid. Loss | \n", "Valid. Accur. | \n", "Training Time | \n", "Validation Time | \n", "
---|---|---|---|---|---|
epoch | \n", "\n", " | \n", " | \n", " | \n", " | \n", " |
1 | \n", "1.84e-02 | \n", "0.0 | \n", "0.0 | \n", "0:04:52 | \n", "0:00:24 | \n", "
2 | \n", "1.49e-06 | \n", "0.0 | \n", "0.0 | \n", "0:04:52 | \n", "0:00:24 | \n", "
3 | \n", "4.64e-07 | \n", "0.0 | \n", "0.0 | \n", "0:04:51 | \n", "0:00:24 | \n", "
4 | \n", "1.43e-07 | \n", "0.0 | \n", "0.0 | \n", "0:04:51 | \n", "0:00:24 | \n", "